Evaluate LLM Code Generation with LLM-as-Judge Evaluators
This tutorial shows how to score AI-generated code against custom criteria to determine the best model for your specific needs, including security, API adherence, and scope creep.
Why it matters
This approach allows organizations to rigorously evaluate AI-generated code against their own custom criteria, leading to more secure and maintainable codebases.
Key Points
- 1Set up a proxy server to route code generation requests through LaunchDarkly and score responses with custom judges
- 2Create judges to check for security vulnerabilities, API contract violations, and unnecessary changes
- 3Use the scoring data to choose the optimal AI model for your codebase and requirements
Details
The article describes a system to evaluate the quality of code generated by large language models (LLMs) like Anthropic's Claude. It involves setting up a proxy server that routes code generation requests through LaunchDarkly, an AI configuration platform. The proxy extracts text-only prompts, selects the appropriate AI model using LaunchDarkly's targeting rules, invokes the model, and triggers custom 'judges' to evaluate the generated code. The judges check for security vulnerabilities, API contract adherence, and unnecessary changes. The scoring data is then used to determine the best model for the user's specific codebase and requirements, rather than relying on generic benchmarks.
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